Control of industrial water treatment via digital imaging

ABSTRACT

A method of analyzing a substrate contacting a fluid present in an industrial system is provided. The method comprises creating a series of digital images of the substrate while contacting the fluid present in the industrial system. A region of interest in the series of digital images of the substrate is defined. A corrosion feature in the region of interest in the series of digital images of the substrate is identified. The corrosion feature in the region of interest in the series of digital images of the substrate is analyzed to determine a corrosion trend of the industrial system. In certain embodiments of the method, the fluid is industrial water, and the industrial system is an industrial water system.

This application is a nonprovisional application claiming the benefit ofU.S. Provisional Patent Application Ser. No. 62/364,130, filed Jul. 19,2016, the disclosure of which is incorporated herein by reference in itsentirety.

BACKGROUND

Standard testing that utilize corrosion coupons can be used to measuregeneral and local corrosion rates in industrial water systems. Standardtesting involves placing an industry-standard corrosion coupon in a testspace (e.g., an industrial water system) and allowing the corrosioncoupon to be exposed to test space conditions, which may cause corrosionof the corrosion coupon. After a period of exposure time, generally30-90 days or longer, the corrosion coupon is removed from the testspace conditions. One or more of a series of tests is then performed todetermine corrosion of the corrosion coupon, which generally correspondsto corrosion found on surfaces of the test space.

Standard testing using corrosion coupons has drawbacks. For example,“real-time” monitoring and analysis is not possible, as the corrosioncoupon(s) are allowed to be exposed to test space conditions with littleor no observation. Should the coupons be located so as to be observed,observation by the naked eye is subjective and generally not capable ofobserving subtle differences in coupons as the onset of corrosion beginsto occur. Additionally, systems for detecting general corrosiontypically lack the ability to detect or predict localized corrosion.

SUMMARY

The invention is directed to using digital imaging of a substrate toanalyze for corrosion in an industrial system, which in certainembodiments is an industrial water system.

A method of analyzing a substrate contacting fluid present in anindustrial system is provided. The method comprises creating a digitalimage of the substrate while the substrate contacts the fluid present inthe industrial system. A region of interest in the digital image of thesubstrate is defined. A corrosion feature in the region of interest inthe digital image of the substrate is identified. The corrosion featurein the region of interest in the digital image of the substrate isanalyzed.

A method of analyzing a substrate contacting fluid present in anindustrial system is provided. The method comprises creating a series ofdigital images of the substrate while the substrate contacts the fluidpresent in the industrial system. A region of interest in the series ofdigital images of the substrate is defined. A corrosion feature in theregion of interest in the series of digital images of the substrate isidentified. The corrosion feature in the region of interest in theseries of digital images of the substrate is analyzed to determine acorrosion trend of the industrial system.

A method of analyzing a substrate contacting industrial water present inan industrial water system is provided. The method comprises treatingthe industrial water of the industrial water system with a corrosioninhibitor. A series of digital images of the substrate is created whilethe substrate contacts the industrial water present in the industrialwater system. A region of interest in the series of digital images ofthe substrate is defined. A corrosion feature in the region of interestin the series of digital images of the substrate is identified. Thecorrosion feature in the region of interest in the series of digitalimages of the substrate is analyzed to determine a corrosion trend ofthe industrial water system, and taking action based on the analysis ofthe corrosion feature in the region of interest in the series of digitalimages of the substrate.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic view of an embodiment of a system that may beutilized to carry out methods described herein.

FIG. 2 is a schematic view of an alternate embodiment of a system thatmay be utilized to carry out methods described herein.

FIG. 3 shows an embodiment of a substrate positioning device that may beutilized in systems and methods described herein.

FIG. 4 is a schematic view of an alternate embodiment of a system thatmay be utilized to carry out methods described herein.

FIG. 5 shows an image of a series of images of an edge view of asubstrate subject to a method described herein.

FIG. 6 is a schematic view of a system that may carry out the methodsdescribed herein.

FIG. 7 shows examples of images, created while practicing a methoddescribed herein, of a substrate undergoing corrosion at four timeintervals.

FIG. 8 shows examples of images subject to a method described herein.

FIG. 9 shows an example of an image of a series of images subject to amethod described herein.

FIG. 10 is a flow chart of logic that is used in an embodiment of amethod described herein.

FIG. 11 shows examples of images subject to a method described herein.

FIG. 12 shows examples of images subject to a method disclosed herein.

FIG. 13 shows an example of an image of a series of images subject to amethod described herein.

FIG. 14 is a graphical illustration of a property of certain corrosionpits present in the image of FIG. 13.

FIG. 15 is a chart of corrosion pit depth versus time for certainexperiments performed on a certain type of substrate.

FIG. 16 shows examples of images subject to a method described herein,which points out certain features of the imaged substrate.

FIG. 17 shows charts of embodiments reflecting analyses of a series ofdigital images, one each for red, green and blue light reflectance.

FIG. 18 shows examples of images, created while practicing a methoddescribed herein, of a substrate undergoing corrosion at six timeintervals.

DETAILED DESCRIPTION

A method of analyzing a substrate contacting fluid present in anindustrial system is provided. The method comprises creating a digitalimage of the substrate while the substrate contacts the fluid present inthe industrial system. A region of interest in the digital image of thesubstrate is defined. A corrosion feature in the region of interest inthe digital image of the substrate is identified. The corrosion featurein the region of interest in the digital image of the substrate isanalyzed.

A method of analyzing a substrate contacting a fluid present in anindustrial system is provided. The method comprises creating a series ofdigital images of the substrate while contacting the fluid present inthe industrial system. A region of interest in the series of digitalimages of the substrate is defined. A corrosion feature in the region ofinterest in the series of digital images of the substrate is identified.The corrosion feature in the region of interest in the series of digitalimages of the substrate is analyzed to determine a corrosion trend ofthe industrial system. In certain embodiments of the method, the fluidis industrial water, and the industrial system is an industrial watersystem.

In a preferred embodiment, the method is a method of analyzing asubstrate contacting industrial water in an industrial water system. Incertain embodiments, the method is a method of quantifying corrosion ofa substrate contacting industrial water in an industrial water system.The phrases “analyzing a substrate,” “defining a region of interest,”“synthesizing trend data,” and “quantifying corrosion of a substrate,”and related terminology (e.g., conjugate forms), are used herein todescribe aspects of the methods, with “analyzing a substrate” beinginclusive of “quantifying corrosion of a substrate,” “defining a regionof interest,” and “synthesizing trend data,” which are all subsets ofanalyzing. The term “substrate,” “corrosion coupon,” and similar termsare to be construed as including “or a portion thereof.”

In certain embodiments of the methods and systems provided herein, thesubstrate is a corrosion coupon. In certain embodiments of the methodsand systems provided herein, the substrate is a section of a conduit. Incertain embodiments of the methods and systems provided herein, thecorrosion coupon is representative of a material of construction of theindustrial water system. In certain embodiments of the methods andsystems provided herein, the substrate, e.g., corrosion coupon, isconstructed of a metal, which may be selected from steel, iron,aluminum, copper, brass, nickel, titanium, and related alloys. The steelmay be mild steel, stainless steel, or carbon steel. In certainembodiments, the brass is admiralty brass. In certain embodiments, themetal is capable of passivation, and in other embodiments the metal isincapable of passivation.

In certain embodiments of the methods and systems provided herein, thesubstrate (e.g., a corrosion coupon) is capable of undergoing a standardcorrosion test, e.g., a corrosion test of the American Society ofTesting and Materials (“ASTM”).

In a preferred embodiment of the methods provided herein, the substratecontacts industrial water present in an industrial water system.Examples of industrial water systems include, but are not limited to,heating water systems (e.g., boiler systems), cooling water systems(e.g., systems comprising a cooling tower), pipelines for watertransport (e.g., seawater transport, which may be in transport to miningoperations), and the like. Industrial water is any aqueous substancethat is or will be used in an industrial water system. Generally,industrial water systems comprise industrial water that may be treatedin some manner to make the water more suitable for use in the industrialwater system of interest. For example, industrial water used in heatingwater systems (e.g., boiler systems) may be deaerated. The industrialwater used in heating water systems may be further treated with acorrosion inhibitor. Other treatments may be rendered for variousindustrial water systems. In certain embodiments of the methods providedherein, the industrial water of the industrial water system is treatedwith a corrosion inhibitor. In certain embodiments of the methodsprovided herein, the industrial water system is a heating water system,which may be a boiler system. In certain embodiments of the methodsprovided herein, the industrial water of the heating water system hasbeen deaerated.

Generally, industrial water is present in an industrial water systemwhen the industrial water is contained or otherwise flowing through aconduit or vessel of the industrial water system. For example,industrial water flowing through a conduit attached to an industrialprocess (e.g., a cooling system, a boiler system, etc.)—whether theconduit be, e.g., a main line conduit, a side stream conduit, a feedline conduit, or an exit line conduit, and so forth—representsindustrial water present in an industrial water system.

Examples of suitable corrosion inhibitors include, but are not limitedto, an azole, a quaternized substituted diethylamino composition, anamine, a quaternary amine, an unsaturated aldehyde, a phosphorus-basedinhibitor composition, a water-soluble molybdenum-containing salt, apoly(amino acid) polymer, an organic sulfonic acid, derivatives thereof(e.g., oxazole, thiazole, etc.), multiples thereof (e.g., more than oneazole), and combinations thereof. In certain embodiments presentedherein, the corrosion inhibitor, in addition to comprising one or moreof the compositions listed in the previous sentence, further comprisesan iodide salt. Examples of suitable iodide salts include, but are notlimited to, lithium iodide, sodium iodide, potassium iodide, calciumiodide, magnesium iodide, ammonium iodide, tetraethylammonium iodide,tetrapropylammonium iodide, tetrabutylammonium iodide,tetrapentylammonium iodide, tetrahexylammonium iodide,tetraheptylammonium iodide, tetraphenylammonium iodide,phenyltrimethylammonium iodide and (ethyl)triphenylphosphonium iodide.In certain embodiments presented herein, the corrosion inhibitor isdosed to the industrial water of the industrial water system in anorganic solvent and optionally a surfactant.

Further examples of corrosion inhibitors are described in U.S. Pat. Nos.9,175,405, 9,074,289, 8,618,027, 8,585,930, 7,842,127, 6,740,231,6,696,572, 6,599,445, 6,488,868, 6,448,411, 6,336,058, 5,750,070,5,320,779, and 5,278,074; U.S. Pat. App. Pub. Nos. 2005/0245411 and2008/0308770; and U.S. Prov. Pat. App. Nos. 62/167,658, 62/167,697,62/167,710, and 62/167,719, the disclosures of each of which areincorporated herein by reference in their entirety for all purposes.

Examples of suitable azoles include, but are not limited to,azole-containing compositions, azoline-containing compositions,derivatives thereof (e.g., oxazoles, thiazoles, acridines, cinnolines,quinoxazolines, pyridazines, pyrimidines, quinazolines, quinolines,isoquinolines, etc.), multiples thereof, and combinations thereof. As itrelates to this disclosure, another way to describe an azole is acomposition having an aromatic, nitrogen-containing ring. Examples ofazole-containing compositions include, but are not limited to,imidazoles, pyrazoles, tetrazoles, triazoles, and the like. Particularlysuitable azoles include, e.g., mercapto-benzothiazole (“MBT”),benzotriazole (“BT” or “BZT”), butyl-benzotriazole (“BBT”), tolytriazole(“TT”), naphthotriazole (“NTA”), and related compositions. Examples ofazoline-containing compositions include, but are not limited to, iminoimidazolines, amido imidazolines, derivatives thereof, multiplesthereof, and combinations thereof. In certain embodiments presentedherein, the azole is quaternized. Examples of azoles are described infurther detail in U.S. Pat. Nos. 5,278,074, 6,448,411, and 8,585,930,which have been incorporated herein by reference.

Examples of suitable substituted diethylamino composition include, butare not limited to, those described in U.S. Pat. Nos. 6,488,868,6,599,445, and 6,696,572, which have been incorporated herein byreference. In certain embodiments presented herein, the substituteddiethylamino composition is quaternized. The substituted diethylaminocomposition may also be an azole, e.g., a quaternized diacrylaminoimidazoline.

Examples of suitable amines (whether quaternized or otherwise) include,but are not limited to, those described in U.S. Pat. Nos. 7,842,127,8,618,027, which have been incorporated herein by reference.

Examples of suitable unsaturated aldehydes include, but are not limitedto, those described in U.S. Pat. No. 7,842,127, which has beenincorporated herein by reference.

Examples of suitable phosphorus-based inhibitor compositions include,but are not limited to, inorganic phosphorus-based inhibitorcompositions, organic phosphorus-based inhibitor compositions,organophosphorus compositions, and combinations thereof. Examples ofinorganic phosphorus-based inhibitor compositions include, but are notlimited to, ADD, and combinations thereof. Examples of organicphosphorus-based inhibitor compositions include, but are not limited to,organic phosphates, organic phosphonates, and combinations thereof.Examples of organic phosphates include non-polymeric organic phosphatesand polymeric organic phosphates. For purposes of this disclosure,“polymeric” describes a composition having repeating units, and“non-polymeric” describes a composition without repeating units.Examples of organic phosphonates include, but are not limited to,2-phosphonobutane-1,2,4-tricarboxylic acid (“PBTC”),1-hydroxyethylidene-1,1-diphosphonic acid (“HEDP”),aminotrimethylene-phosphonic acid, monosodium phosphinicobis (succinicacid), ADD. Examples of organophosphorus compositions includephosphines.

Examples of suitable organic sulfonic acids include, but are not limitedto, those described in U.S. Pat. No. 8,618,027, which has beenincorporated herein by reference. Examples of suitable organic sulfonicacids include, but are not limited to, benzenesulfonic acid,dodecylbenzenesulfonic acid (“DDBSA”), and preferably branched DDBSA.

Examples of suitable water-soluble molybdenum-containing salts include,but are not limited to, alkali molybdates, e.g., sodium molybdate,potassium molybdate, ammonium molybdate, strontium molybdate, and thelike.

In certain embodiments, the poly(amino acid) polymer has a hydroxamicacid-containing sidechain. An example of a suitable poly(amino acid)polymer having a hydroxamic acid-containing sidechain includes, but isnot limited to, that of general Formula (I):

wherein W is CO₂M^(x) or CONHOH, wherein M^(x) is a metal ion; Y isCH₂CONHOH or CH₂CO₂M^(y), wherein M^(y) is the same or different metalion as M^(x); M¹ is an alkali metal, an alkaline earth metal orammonium; (a+b)/(a+b+c+d)*100%+(c+d)/(a+b+c+d)*100%=100% ranges fromabout 0.1% to about 100%, preferred 5%-70%, more preferred 10%-50%;(c+d)/(a+b+c+d)*100% ranges from 0% to 99.9%; a/(a+b)*100% ranges from0% to 100%; b/(a+b)*100% ranges from 0% to 100%;a/(a+b)*100%+b/(a+b)*100%=100%; c/(c+d)*100% ranges from 0% to 100%;d/(c+d)*100% ranges from 0% to 100%; c/(c+d)*100%+d/(c+d)*100%=100%; andthe molecular weight ranges from about 300 to about 200,000 daltons.Further examples of suitable poly(amino acid) polymers having ahydroxamic acid-containing sidechain are described in U.S. Pat. No.5,750,070, which has been incorporated by reference.

The corrosion inhibitor may be present in the industrial water at aconcentration of from about 0.01 ppm to about 1000 ppm by weight,including from about 0.1 ppm or from about 1 ppm, to about 500 ppm, orto about 200 ppm.

In certain embodiments of the methods provided herein, a parameter ofthe industrial water system is measured. Parameters include, but are notlimited to, temperature, pressure, pH, conductivity, oxidation-reductionpotential, linear polarization resistance, derivatives thereof, andcombinations thereof. In a preferred embodiment, the methods describedherein further comprise measuring linear polarization resistance of thefluid in the industrial system, and acting based on at least one of theanalysis of the corrosion feature in the region of interest of thedigital image, or series thereof, of the substrate, and the measuredlinear polarization resistance of the fluid of the industrial system. Ina preferred embodiment, the invention is directed to using digitalimaging of a substrate and linear polarization resistance to analyze forcorrosion in an industrial water system.

The substrate is sufficiently lit to allow for creation of digitalimages of the substrate located in the industrial water system. Inpreferred embodiments, the substrate is sufficiently lit using alight-emitting diode, and, more preferably, a plurality oflight-emitting diodes.

In certain embodiments of the methods disclosed herein, a series ofdigital images of the substrate is created. In certain preferredembodiments, the series of digital images of the substrate is createdwhile the substrate is located in an industrial system, e.g., anindustrial water system. Though not preferred, the series of digitalimages of the substrate can be created while the substrate is notlocated in an industrial system. In the preferred embodiments, thesubstrate located in the industrial system, e.g., an industrial watersystem, is generally in contact with a fluid, e.g., industrial water.

When utilized, the series of digital images may be two or more digitalimages. In certain embodiments of the methods provided herein, theseries of digital images comprises a quantity of digital imagessufficient to perform trend analysis of the digital images, and thus ofthe substrate. In preferred embodiments of the methods provided herein,the series of digital images is a quantity sufficient to performcorrosion trend analysis of the substrate. In certain embodiments of themethods provided herein, the series of digital images is created at afixed time interval, i.e., each image is taken after a fixed amount oftime has elapsed. In certain embodiments of the methods provided herein,the series of digital images is created at a fixed time interval when aparameter of the industrial system, e.g., industrial water system, iswithin a control limit, but the series of digital images is created atan interval of time less than the fixed time interval when the parameterof the industrial system is not within the control limit. In otherwords, when the process is in control, a digital image is created at arate of one digital image per t-length of time, but when the process isout of control, a digital image is created at a rate faster than onedigital image per t-length of time.

In certain embodiments of the methods provided herein, the digitalimage, or series thereof, of the substrate is analyzed to determine acorrosion trend of the substrate in the industrial system, e.g.,industrial water system. In certain embodiments, analyzing comprisesdefining a region of interest in the series of digital images of thesubstrate and synthesizing trend data of the region of interest from theseries of images. In some embodiments, analyzing comprises mathematicaltransformation of data to synthesize information related to size (e.g.,a one-dimensional measurement or surface area calculation to infer pitdepth), color profile, number of corrosion features, percent areacovered by corrosion features, overall mean surface area of corrosionfeatures, percent active corrosion features, and combinations thereof,to calculate a corrosion trend (e.g., a localized corrosion rate).Localized corrosion and examples of mathematical transformations of dataare discussed further herein. In certain embodiments of the methodsprovided herein, the method further comprises estimating pit depth ofthe corrosion feature based on the estimated surface area of thecorrosion feature. In certain embodiments of the methods providedherein, the method further comprises estimating pit depth of thecorrosion feature based on a one-dimensional measurement of thecorrosion feature. Examples of one-dimensional measurements of acorrosion feature includes, but is not limited to, length (e.g., apoint-to-point measurement across a corrosion feature), perimeter (e.g.,circumference around a corrosion feature), and similar measurements andestimates thereof.

In certain embodiments, the methods comprise defining a region ofinterest in the digital image, or series thereof, of the substrate. Theregion of interest may comprise a surface of the substrate. In certainembodiments of the methods provided herein, the region of interest is asurface, or portion thereof, of a substrate (e.g., a corrosion coupon).

In certain embodiments of the methods provided herein, the region ofinterest comprises one or more corrosion features. In certainembodiments of the methods provided herein, a plurality of corrosionfeatures is identified in the region of interest. The corrosion featuresmay be counted and/or tracked for changes in number, which can provideinformation related to the corrosive environment that may be present inthe industrial system, e.g., industrial water system. In certainembodiments, the method comprises identifying a corrosion feature in theregion of interest, which may further comprise predicting a futurecorrosion event based on the corrosion feature. In certain embodimentsof the methods provided herein, the surface area of the corrosionfeature is calculated, which allows for a prediction of pit depthestimated based on the surface area of the corrosion feature.

Localized corrosion tends to form pits in material surfaces, and thus issometimes called “pitting” corrosion. Localized corrosion can bedescribed as a stochastic process with variable rates. Generally,localized corrosion is responsible for many industrial system failures,particularly related to industrial water systems. While generalcorrosion of industrial systems may be somewhat predictable usingconventional corrosion monitoring (e.g., linear polarization resistance,(“LPR”)), localized corrosion has been more difficult to monitor and/orpredict in real time, generally requiring sophisticated instrumentationand analytical procedures. In certain embodiments of the methodsprovided herein, the corrosion trend determined for the industrialsystem is a localized corrosion trend.

In certain embodiments, a potential future corrosion event is predictedbased on the analysis, or subsets thereof, of the series of digitalimages. In certain embodiments of the methods provided herein, thepotential future corrosion event is any one or more of the following:corrosion rate, corrosion failure, and combinations thereof.

In certain embodiments of the methods provided herein, action is taken(i.e., “acting”) based on the analysis of the corrosion feature in theregion of interest of the digital image, or series thereof, of thesubstrate. Generally, the action taken will be one or more action toprevent or lessen the effects of corrosion (preferably localizedcorrosion) in the industrial system, e.g., an industrial water system.Any one or more actions may be taken, including, but not limited to,increasing dosage of corrosion inhibitor, selecting a differentcorrosion inhibitor, modifying the corrosion inhibitor, altering aphysical property of the industrial system, shutting down the industrialsystem, and combinations thereof.

In certain embodiments of the methods provided herein, time scale and/orend-point measurement limitations of substrate monitoring are addressedby integrating an imaging system into the industrial system, e.g., anindustrial water system. In certain embodiments of the methods providedherein, the substrate is a corrosion coupon, and the imaging system isintegrated as part of a standard coupon rack. In certain embodiments ofthe methods provided herein, the imaging system is non-intrusive. Incertain embodiments of the methods provided herein, the imaging systemprovides the ability to capture real-time corrosion activity on thesurface of a coupon contacting a fluid (e.g., industrial water) presentin an industrial system (e.g., an industrial water system. For example,FIG. 1 shows a portion of an industrial system, in this example, anindustrial water system, comprising imaging system 1 attached to theindustrial water system at a process flow pipe. The portion of theindustrial water system comprises pipe 100 that transports a fluid, inthis example, industrial water, to substrate 101 (e.g., a corrosioncoupon) held in the pipe by substrate holder 102 connected topass-through 103 inserted into tee 104. Substrate 101 may be constructedof a metal that is representative of the wetted materials ofconstruction of the industrial water system being monitored, which incertain embodiments comprises carbon steel, brass (e.g., admiraltybrass), stainless steel, aluminum and/or related alloys. Other selectionoptions are that one or more surfaces of the substrate have a certainfinish, e.g., ground, sand blasted, polished, etc., and whether or notthe substrate is passivated. Components 100-104 may partially orentirely comprise standard coupon mounting hardware used in commerciallyavailable corrosion coupon racks (e.g., EnviroAqua Consultants Inc.,7116 Sophia Ave, Van Nuys, Calif., Model ACR-22) designed according toASTM specifications.

The imaging system requires optical access to view the substratecontacting the process fluid stream, i.e., the industrial water.Generally, commercial coupon rack systems use clear PVC pipe to provideoperators the ability to visually inspect a corrosion coupon, whichallows for direct mounting of the imaging system. If the pipe is opaque,then modifications are required such as installing a clear PVC pipesection or modifying the pipe to provide optical access. FIG. 1 showsoptical access as window 105.

FIG. 2 shows an alternate embodiment of imaging system 1, which includesmany of the same features as the embodiment illustrated in FIG. 1. Forexample, the imaging systems of FIGS. 1 and 2 comprise camera 106, whichmay be a complementary metal-oxide-semiconductor (“CMOS”) or acharge-coupled device (“CCD”) camera, equipped with lens 107. In theembodiments of FIGS. 1 and 2, camera 106 is mounted on fixture 108 vialinear translation stage 109, which allows for adjustment of focus.Alternatively, a camera with an autofocus feature such as, e.g., TheImaging Source camera model DKF72AU02-F (6926 Shannon Willow Road,Charlotte, N.C. 28226) can be utilized, obviating the need for lineartranslation stage 109. Camera 106 can be black and white or preferablycolor to provide additional insight into corrosion dynamics. In theembodiments of FIGS. 1 and 2, light sources 110 are used to illuminatethe coupon, which may not be necessary depending on natural and/or otherartificial light available at any particular location.

Multiple light sources may be used to illuminate from differentdirection to accentuate the desired features on the substrate or surfacethereof, or to improve the overall illumination profile. For example,illuminating a surface of the substrate with a light source positionednear perpendicular to the surface can provide a bright fieldillumination. In this case, the imaging device captures most of thedirect reflected light. Placing one or more light sources with largeangle(s) of incidence relative to the surface normal can enhance salientfeatures, such as scratches or pits, on the surface. In addition, thelight can be directional or diffuse. Diffuse lighting provides moreuniform illumination and attenuates the specular component whenilluminating reflective surfaces. The light may be sourced from one ormore of a light emitting diode (“LED”), an incandescent bulb, a tungstenhalogen bulb, light transported via fiber optic or any combination ofthese or other standard means to provide illumination. In certainembodiments of the systems and methods provided herein, four LED lightsources are utilized and arranged such that each of the four LED lightsources directs light in an X pattern toward the substrate, an exampleof which is shown in FIG. 2.

An example of an LED light source is available as CREEXPE2-750-1 fromCree, Inc., 4600 Silicon Drive Durham, N.C. 27703, which in certainembodiments is equipped with a Carclo lens model 10138, available fromCarclo Optics, 6-7 Faraday Road, Rabans Lane Industrial Area, AylesburyHP19 8RY, England, U.K.

In the embodiments of FIGS. 1 and 2, light sources 110 are mounted tomounts 111 that allow for angle and height adjustment. The lightemission wavelength spectrum can cover the white light region orspecific wavelength bands to highlight specific features. For example,specific wavelengths can be used to highlight color on the substratesurface or used with black and white camera to extract color informationfrom the surface. In certain embodiments of the methods presentedherein, the substrate is lit with light having a wavelength band of fromabout 390 nm to about 700 nm.

Image acquisition control can be made by a PC, microprocessor, externalcontroller, and/or embedded processor on the camera. Commercial digitalcameras generally come standard with image acquisition speeds 30 framesper second (“fps”) or greater. Because corrosion generally occurs at amuch longer time scale (e.g., 10s of minutes to weeks), imageacquisition control is the preferred method, i.e., acquiring a singleimage or average of N images at a frequency that can be, e.g., fixed,variable, and/or event driven. Collecting data in this manner utilizesdata storage more efficiently. For example, an image acquisition rate ofonce per day, or once per week, may be sufficient for certain industrialsystems if only gross changes in corrosion features are of interest.However, if the industrial system experiences an upset, e.g., a drop inpH, the dynamics of the corrosion features can be missed with infrequentimage acquisition. In this case, triggering an increase in the frequencyof the creation of the digital images at the time of upset allows forcollecting image data at a finer time resolution.

Interfacing the imaging system to a fluid stream in an industrial system(e.g., to a stream of industrial water in an industrial water system)can be done by directly mounting the imaging system on a process pipe,as shown in FIGS. 1 and 2, using, e.g., mounting clamps 112. Bottomplate 113 and enclosure housing 114 provide protection to the internalcomponents from the environment. Additionally, bottom plate 113 andenclosure housing 114 control ambient light from interfering with thelight produced by light sources 110. Electrical power and/orcommunication can be provided to components of the imaging system bycabling connections and/or antennae.

Additional illumination control can be provided via the utilization offilters and/or polarizers on light source(s) 110 and/or imaging device106. For example, adding linear polarizers 115 and 116 allows for theremoval of reflections or hot spots (e.g., high light intensity glare)from the image originating from the light source rays that, e.g., mayreflect off the transparent window or pipe. Additionally or instead,color filters (e.g., bandpass, notch, shortpass, and/or longpass) may beused to enhance specific image detail or remove background lighteffects. Filtering can be applied on the camera, light source, or both.For example, red features on a surface can be enhanced using a lightsource with a bandpass or longpass filter greater than 600 nm, e.g.,600-1100 nm, or more preferably 600-700 nm, and even more preferably,630 nm. In this case, the red light will reflect off the red surfaces ofthe substrate to the imaging detection device that can also be equippedwith a similar filter. This allows only the reflected light from thesurface in the wavelength transmission range of the filter to reach thedetector, resulting in red feature enhancement.

In certain embodiments, the methods provide the ability to monitormultiple locations of the substrate. For example, a plurality of camerasand light sources mounted at different positions relative to thesubstrate can provide the ability to image different sides, edges, andangles of the substrate (e.g., coupon).

Alternatively, as shown in FIG. 3, substrate positioning device 300 maybe utilized, which allows substrate 103 to be rotated to differentpositions to image both sides of the substrate (front and back) as wellas a side and/or angled views. The system shown in FIG. 3 comprisessubstrate positioning device 300 attached to substrate holder 102 thatis inserted through pass-through 304. Pass-through 304 uses seals 301(e.g., O-rings) to provide a seal and allow substrate holder 102 torotate. Otherwise, imaging system 1 of FIG. 2 is the same configurationas system 1 as shown in FIG. 1. Substrate positioning device 300 can bemanual control, servomotor, or stepper type to control the couponposition.

Another example of substrate positioning device 300 is shown in FIG. 4,which for this embodiment is constructed of a keyed plug modified to beattached to substrate holder 102, which attaches to substrate 103.Substrate holder 102 and substrate 103 are inserted through pass-through304. Pass-through 304 uses one or more seals 301 to provide a seal andallow substrate holder 102 to rotate. Like in the embodiment of FIG. 3,substrate positioning device 300 of FIG. 4 can be manual control,servomotor, or stepper type to control the coupon position. Thesubstrate positioning devices of FIGS. 3 and 4 may be utilized as partof the systems of either of FIGS. 1 and 2.

An example case where the substrate is imaged at a different position isshown in FIG. 5 for a side view of a mild steel coupon exposed to WaterA for 22 days. Imaging the side of the coupon allows for the capture ofdetails about the height (maximum height) of the corrosion productsformed on the coupon surface. The magnitude of the height and monitoringthe height change in time provides insight on the level of corrosionactivity, e.g., a large change in height suggesting an increased levelof corrosion activity.

In certain embodiments, a plurality of imaging devices is utilized tocreate a plurality of digital images, or series (plural) thereof, of oneor more substrates. For example, multiple imaging systems can be mountedon an industrial water system to monitor at different points and/orvaried substrate metallurgy. FIG. 6 shows an example of a coupon rackwith 4 coupon mounting points 400 further comprising a coupon holderrod, holder nut, and coupon, though the substrate positioning devices ofeither of FIGS. 3 and 4 could be utilized. The coupon rack is outfittedwith three imaging systems 1 (labeled 1 a-1 c to differentiate each fromthe others) as previously described and shown in FIGS. 1 and 2. Theimaging systems interface directly to controller 404 that can be a PC,microprocessor, gateway, or combination of such devices to establishelectronic communication for acquisition control as well as store and/ortransmit image data. FIG. 6 shows cabling 405 connecting imaging systems1 a and 1 b. In certain embodiments (e.g., imaging system 1 a and 1 b),cabling 205 provides power and bi-directional data transfer, i.e.,collect image data or send commands to control digital camera settings.Alternatively, a wireless protocol (e.g., one or more of Wi-Fi, Zigbee,LoRa, Thread, BLE OnRamp, RPMA, the EEE 802.11 network family, IEEE802.15.4, Bluetooth, HiperLAN, etc.) can be used to communicate betweenthe imaging device and controller 404, as shown for imaging device 1 cequipped with a wireless communication device communicates to controller404 via antennae 406. Powering the imaging units can be through cable405, battery, solar, or other energy harvesting means, e.g., vibrationor thermal. The combination of using a wireless protocol with aself-powered method allows convenient installation at multiplelocations. Image data collected by controller 404 can be stored,processed using advanced image analysis algorithms, processed andreduced to key trending variables, transmit data to a remote server, orcommunicate with a control device, e.g., a distributed control system(“DCS,” e.g., Nalco 3D technology, available from Nalco Water, an Ecolabcompany, 1601 West Diehl Road, Naperville, Ill. 60563), a laboratoryinformation management system (e.g., a “LIMS” software/hardwarepackage), and/or a cloud computing system.

Creating the digital image can be acquired by simply taking a snap-shotof the substrate, and a series of digital images can be acquired bytaking two or more snap-shots of the substrate over time. In certainembodiments, the digital images of the series of digital images areaveraged, which can provide improved signal-to-noise ratio, as shown inFIG. 7, which, for example, may be used to create a time-lapse videosynchronized to process data collected by measuring a parameter of theindustrial water in the industrial water system. The method may furthercomprise analyzing (e.g., synthesizing) the data collected from thedigital image, or series thereof, by mathematically transforming thedata, which in certain embodiments may provide further insight on thedetected corrosion. For the simple snapshot data collection shown inFIG. 7, a set of four images are shown covering a period of 21 days fora pretreated mild steel coupon. In this case, the coupon was exposed towater with the following composition (an example of industrial water,hereinafter “Water A”):

TABLE 1 The composition of Water A. Concentration (in ppm Concentration(in ppm Water A contents as CaCO₃) as the substance) Calcium 450 180Magnesium 225 54 Alkalinity 100 122 Chloride 600 426 Sulfate 225 216

The Water A was treated with 100 ppm of a corrosion inhibitor comprising4.5% ortho-phosphate, 4.5% phosphine succinc oligomer, 1.2%benzotriazole, 0.3% tolyltriazole, and 5.4% tagged high stress polymer(available from Nalco, an Ecolab Company, as 3DT189 corrosioninhibitor). Changes in the corrosion features on the coupon surface areclearly visible in the digital images of FIG. 7 as indicated by the darkareas against the coupon background. The size and appearance of newfeatures is observed for the 21-day test. The ability to capture thecoupon image at different times provides a means to monitor the changesoccurring on the coupon surface, in this instance, due to corrosion.Furthermore, the ability to store image data provides the ability tocompare current image data to past observations of different substratesof all kinds, e.g., similarly-situated substrates in the same industrialwater system, similarly-situated substrates in different industrialwater systems, statistical analyses of a population of substrates, andthe like. For example, a series of digital images of a substrate can becreated every 5, 10, 15 . . . days and analyzed against historicaldigital image data collected at the same incremental periods for one ormore substrates located at the same position within the industrial watersystem. Observed differences between the data can indicate changes inthe process due to the treatment program and/or water quality.

Utilizing digital image-processing algorithms can provide quantitativeevaluation of the digital images, which provides quantitative evaluationof the corrosion of the substrate, and therefore of the corrosion of theindustrial system. Data collected from the series of digital images canbe used to develop overall trends related to a feature (or pluralitythereof) or changes on the substrate surface area.

An example outlining the steps to identify the number of corrosionfeatures and average size is shown in FIG. 8. A region of interest isdefined to limit the analysis of the series of digital images of thesubstrate. A threshold analysis is applied to identify corrosionfeatures and reduce the N-bit image to a binary image, as shown in thelower left-hand quadrant of FIG. 8. from the binary image in FIG. 8, aclear distinction between the substrate where no corrosion activity ispresent (black background) and the corrosion features (white) can beobserved. The surface areas of the corrosion features are calculated andbinned to generate a distribution. From the distribution, generaldescriptive statics such as mean, standard deviation, range, etc., maybe calculated and stored with the corresponding time stamp. Performingthe steps on each image of a series of images allows for plotting thereduced data, e.g., as a trend plot for the average area and featurecount (see, e.g., FIG. 9).

In certain embodiments, two-step threshold processing is applied (suchas the one in the previous example) to identify the corrosion feature(s)involved. Two-step threshold processing made on each image accounts forvariations in background and changes in the percent area coverage of thecorrosion feature(s). The processing involves applying a coarsethreshold to the digital image to locate corrosion features. For theprevious example, the area of each feature from the coarse threshold isgreater than the true area. Image masking is applied to the coarsethreshold areas to remove the features from the image. An intensityhistogram is calculated to determine the intensity distribution with nocorrosion features, i.e., substrate background only. To determine thecorrosion feature a fine threshold setting may be calculated using 3σthreshold values from the background distribution. For example, applyingthe calculated 3σ threshold values to the distribution in FIG. 8 usingthe 2-step threshold approach allows for identification of corrosionfeatures. In certain embodiments, image processing methods usingnormalization and/or edge identification to detect sharp transitionsbetween the background and corrosion feature(s) are used.

In certain embodiments, plotting variables such as percent area coverageand/or ratio of the average area divided by the number of features canalso be created. Percent area coverage is based on the ratio of theoverall corrosion feature area (sum of the area for all featuresidentified) divided by the area of the region of interest. This providesa metric for the level of corrosion covering the surface.

The ratio of the average area divided by the number of features providesan indication on the type of corrosion, i.e., general versus localized.For example, two substrates with the same summed area of corrosionfeatures is not descriptive regarding the type of corrosion. Byincluding the feature count and developing a ratio of the summed areadivided by the count, forms a new variable, which provides insight onthe degree of localized corrosion. For this example, the substrate withthe higher corrosion feature count would have a ratio value less thanthe case with fewer features indicating localized corrosion is morepredominate.

Additional variables can be also be created by combining the corrosiondata associated with the series of digital images with data obtainedfrom corrosion monitoring probes, e.g., a Nalco corrosion monitoring(NCM) probe based on linear polarization resistance (“LPR”). LPR is astandard tool used for instantaneous general corrosion monitoring totrend the mils per year (“mpy”) for different metallurgies. By analyzingdata from a plurality of sources an estimated real-time localizedcorrosion rate and classification scheme for alarming can be created.For example, an alarming scheme developed following the data in Table 1from Mars G. Fontana⁵ (Corrosion Engineering, 3rd Edition) provides anexample of classifying the level of localized corrosion. The dataprovides a starting point to develop an alarming scheme to alert userson the severity of localized corrosion and take proper corrective actionearly if needed. Additionally, the localized corrosion informationcorrelated with events can be used as a troubleshooting tool. Forexample, for an industrial water system, an increase in localizedcorrosion after a make-up water change may indicate that the waterquality is more corrosive than the previously used make-up water.Corrective action can be as simple as adding additional and/or adifferent corrosion inhibitor, or, in more severe cases, passing themake-up water through an ion-exchange column may be necessary to reducethe corrosivity of the make-up water.

TABLE 2 Localized corrosion rate classification for mild steel, allvalues are approximate. Relative corrosion resistance of commonferrous-and nickel-based alloys mpy mm/yr μm/yr nm/hr Outstanding  <1<0.02  <25  <2 Excellent 1-5  0.02-0.1   25-100  2-10 Good 5-20 0.1-0.5100-500 10-50 Fair 20-50  0.5-1   500-1000  50-150 Poor 50-200 1-51000-5000 150-500 Unacceptable >200 >5   >5000 >500

For mild steel, corrosion pit depth estimation from analyzing the seriesof digital images follows the processing flow chart listed in FIG. 10.First, the upper limit pit depth is estimated assuming that once a pitis initiated it grows continuously with mass transport or diffusion asthe rate-controlling factor. For a well-defined pit, this is believed tobe the worst-case scenario. For pretreated mild steel coupons havingdouble-ground finish, it was found that the upper limit pit depth can beestimated using the following mathematical transformation (CorrosionScience 50, 2008, 3193-3204):d=1.4+13.3t ^(0.5)  (1)where t is expressed in days and the pit depth d in μm.

Substrate analysis from laboratory and field tests indicates theestimated upper limit pit depth d from Eq. (1) is always greater thanthe actual pit depth measurement. For coupons constructed of a differentmetallurgy and surface finish, an upper limit pit depth can be obtainedempirically.

Furthermore, a heuristic calibration factor developed from offlinesubstrate analysis, e.g., coupon removed from service and cleaned, showsthat, for well-defined isolated pits (e.g., those having a sharp colorchange as compared to the background of the substrate), the pitequivalent diameter to depth ratio for metal coupons exposed todifferent conditions and durations is m:1, where m is from about 1 toabout 30. Generally, the value of m depends on metallurgy, fluid flowconditions and corrosion inhibitor treatment conditions. For example,assuming typical conditions for a cooling water system, for mild steelcoupons, m is about 5, and for admiralty brass coupons, m is about 15.Thus, the pit depth can be inferred from the pit area, except in thecase where pits begin to overlap or large tubercles form due tounder-posit, which would result in much larger equivalent pit diameterthan those of well-defined pits. The exception condition can be definedas maximum pit diameter divided by m larger than the upper limit pitdepth. Alternative approaches for pit depth calculation are presentedherein to address the exception.

Because corrosion 1) generally happens at n discrete pit regions withareas of s₁, s₂, . . . , s_(n), and depth of d₁, d₂, . . . , d_(n), thetotal area in the field of view of each digital image (which in certainembodiments makes up the region of interest) is S_(fieldofview); and 2)generally results in pits that are hemisphere or semi-ellipsoidal inshape, the volume of each pit is equal to ⅔s_(i)d_(i), where i=1 to n.Thus, the averaged pit depth d weighted by pit areas can be expressed asthe following mathematical transformation:

$\begin{matrix}{\overset{\_}{d} = {\frac{\sum\limits_{i = 1}^{n}{s_{i}d_{i}}}{\sum\limits_{i = 1}^{n}s_{i}} = {{\frac{3}{2}\frac{\sum\limits_{i = 1}^{n}{\frac{2}{3}s_{i}d_{i}}}{\sum\limits_{i = 1}^{n}s_{i}}} = {\frac{3}{2}\frac{V_{total}}{S_{total}}}}}} & (2)\end{matrix}$where V_(total) is the total metal loss from the total area in the fieldof view and S_(total) is the total corroded area in the total area ofthe field of view.

If the metal loss, V_(total), is uniformly distributed inS_(field of view), the depth is a general corrosion depth, d_(general),can be calculated with the following mathematical transformation:

$\begin{matrix}{\overset{\_}{d} = {{\frac{3}{2}\frac{V_{total}}{S_{total}}} = {{\frac{3}{2}\frac{d_{general}S_{{field}\mspace{14mu}{of}\mspace{14mu}{view}}}{S_{total}}} = {\frac{3}{2}\frac{d_{general}}{P_{corr}}}}}} & (3)\end{matrix}$where P_(corr) is percentage of corroded area in the field of view.According to Eq. (3), the average localized corrosion depth would beproportional to the reciprocal of percentage of corroded area.

Although d_(general) is unknown, it can be calculated based on LPR data.The assumption is that the general corrosion depth, d_(general), of apretreated substrate is proportional to integrated LPR corrosion rate,χ, times the total immersion time, t, according to the followingmathematical transformation:d _(general) =αχt  (4)where α is a calibration factor, χ is LPR general corrosion rate, and tis the total immersion time. Therefore, the average localized corrosionrate is obtained by combining Eq. (3) and (4) to obtain the mathematicaltransformation of Eq. (5):

$\begin{matrix}{\overset{\_}{r} = {\frac{\overset{\_}{d}}{t} = {\frac{3}{2}\frac{\alpha\chi}{P_{corr}}}}} & (5)\end{matrix}$where r is averaged localized corrosion rate, d is averaged pit depthweighted by pit areas, α is a calibration factor, i.e. a constant, χ isintegrated LPR corrosion rate, t is the total immersion time, P_(corr)is percentage of corroded area in the field of view.

An example using the above analysis is shown in FIG. 11 for LPR anddigital imaging data collected on a mild steel coupon to estimate theintegrated local corrosion value in mils per year. Changes in thecorrosion features on the substrate surface are shown at differenttimes. The alarm scheme developed to assess localized corrosion (i.e.,localized corrosion measurement, or “LCM”) according to the guidelinesset forth in Table 1. During the first 10 days, the LCM remained lowindicating good corrosion resistance with only a few minor excursionsinto the fair region. However, at a longer period the LCM continuedupward into the poor corrosion resistance region. A breakdown of thepercentage of time spent under the different corrosion resistanceregions is also shown. This information provides a quick assessment onthe treatment program effectiveness and identifies periods whencorrosion control was poor and for how long. This example illustrateshow the combination of digital imaging over time and LPR measurement canbe used to alarm operators of the corrosion stress in the system andprovide analysis for feedback control, which may comprise changing thedosing amount or treatment program. The example also illustrates amethod to collect data dynamically and reduce the data to a trendingvariable for tracking, alarming and feedback control.

The integrated localized corrosion rate estimate provides an example ofa mathematical transformation that yields an indication of the level oflocal and general corrosion. An additional or alternative approach usesthe combination of digital imaging and LPR data based on the premisethat corrosion is a slow process and detecting changes in the pit areaand/or depth occurs gradually over time. For example, if the localizedcorrosion rate is, e.g., about 100 mpy (i.e., about 290 nm/hr), then thepit depth will take 16 hours to increase 4.6 μm. Using the heuristicratio of 5:1 for pit diameter to depth, the pit diameter would increase23 micron after 16 hours for this case, which is detectable by digitalimaging. However, detecting instantaneous localized corrosion eventsbased on image analysis alone is limited because of the gradualoccurrence of corrosion over time.

A second approach is to extend the analysis to develop an instantaneouslocalized corrosion rate by differentiating Eq. (5) with respect to timeto get the following mathematical transformation:

$\begin{matrix}{r = {\frac{\partial\overset{\_}{d}}{\partial t} = {\frac{\partial\left( {\frac{3}{2}\frac{{\alpha\chi}\; t}{P_{corr}}} \right)}{\partial t} = {{{\frac{3}{2}\frac{\alpha}{P_{corr}}\frac{{\partial\chi}\; t}{\partial t}} - {\frac{3}{2}\frac{{\alpha\chi}\; t}{P_{corr}^{2}}\frac{\partial P_{corr}}{\partial t}}} = {{\frac{3}{2}\frac{\alpha}{P_{corr}}\delta} - {\frac{3}{2}\frac{{\alpha\chi}\; t}{P_{corr}^{2}}\frac{\partial P_{corr}}{\partial t}}}}}}} & (6)\end{matrix}$where r is real-time localized corrosion rate, α is a calibrationfactor, i.e., a constant, δ is real-time LPR corrosion rate, P_(corr) ispercentage of corroded area in the field of view (e.g., region ofinterest). Generally, the area change for a pit occurs gradually, as aresult change in P_(corr) over a short time period is approximatelyzero, simplifying Eq. (6) to the following mathematical transformation:

$\begin{matrix}{r \approx {\frac{3}{2}\frac{\alpha}{P_{corr}}{\delta.}}} & (7)\end{matrix}$Here r is real-time average localized corrosion rate, α is a calibrationfactor, i.e., a constant, δ is real-time LPR corrosion rate, andP_(corr) is percentage of corroded area in the field of view (e.g.,region of interest).

Generally, given all factors being constant, pit depth growth rate isnot constant: initially occurring at a faster rate and then plateauingover time. From Eq. (2) the pit depth is proportional to t^(0.5), i.e.,

$\begin{matrix}{d \propto {t^{0.5}\mspace{14mu}{and}}} & (8) \\{{\frac{\partial d}{\partial t} \propto {t^{- 0.5}\mspace{14mu}{thus}}},} & (9) \\{{r \propto t^{- 0.5}},} & (10)\end{matrix}$each of which is a mathematical transformation, where d is pit depth, ris real-time average localized corrosion rate and t is the totalimmersion time. Therefore, the projected corrosion rate after threemonths service can be obtained based on a shorter time treatment usingEq. (10). For example, the ratio of the projected real-time averagelocalized corrosion rate after three month (30-day months) treatment tothe real-time average localized corrosion rate at time t can beexpressed as the following mathematical transformation:

$\begin{matrix}{\frac{r_{projected}}{r} = {\frac{90^{- 0.5}}{t^{- 0.5}} = {\frac{t^{0.5}}{90^{0.5}}.}}} & (11)\end{matrix}$Using Eq. (11), the corrosion rate of 100 mpy after three days treatmentis equivalent to 18 mpy after 90 days. Eq. (11) can be combined with Eq.(7) to give the following mathematical transformation:

$\begin{matrix}{r_{projected} \approx {\frac{3}{2}\frac{\alpha}{P_{corr}}\delta\frac{t^{0.5}}{90^{0.5}}}} & (12)\end{matrix}$where r_(projected) is a normalized real-time average localizedcorrosion rate for 90 days, α is a calibration factor, i.e., a constant,δ is real-time LPR corrosion rate, P_(corr) is percentage of corrodedarea in the field of view, and t is total immersion time.

An example applying the concept of a normalized real-time averagelocalized corrosion rate is shown in FIG. 12 along with data from thestandard LPR measurement from FIG. 11 data. In FIG. 12, the combinationof imaging data and LPR has been used to rescale the data to reflect thelocalized corrosion activity. The initial normalized LCM result isgreater than 60 mpy with a Nalco Corrosion Monitor (“NCM”) reading<2 mpyindicating that localized corrosion is dominating consistent with thedigital image data that shows only a few very small active sites. Astime progresses, the number of corrosion sites identified by digitalimaging analysis increases and the normalized LCM and LPR values areapproximately 55 mpy and approximately 10 mpy respectively. Thissuggests that the density of corrosion features is relatively high,e.g., area coverage approximately 10%, indicating that both localizedand general corrosion are present.

A further aspect of the methods set forth herein is to track thecorrosion surface area change and integrated time for individualcorrosion features. Using digital imaging analysis in combination withother sensor data, e.g., pH, conductivity, ORP, LPR, etc., can allow forshortening of evaluation time for a corrosion treatment program. Incertain circumstances, limited experimental evidence may suggest thatpit depth estimation or corrosion rate can be obtained much sooner thanthe typical substrate service period where information is obtained onlyafter the substrate (e.g., coupon) is removed from service. An examplesupporting this finding is shown in FIGS. 13-15, where individualtubercles are identified and tracked over time. FIG. 13 shows anormalized time averaged tubercle features captured by digital imagingafter approximately 15 days exposure to Water A treated with 100 ppm3DT189. The gray scale is normalized to the total coupon immersion time.For example, the light-shaded area indicates the feature has beenpresent the longest whereas appearance of the darker color is morerecent, as indicated by FIG. 13. The light dark color is an indicationof the corrosion feature, i.e., tubercle area, is actively expanding. Byusing the time averaged area image, identification and number of activetubercles can be quickly located. FIG. 14 shows the area change for eachtubercle corresponding to the labeled feature in FIG. 13. For example,for the tubercle labeled 14, the normalized time averaged area in FIG.13 is light colored indicating little if any change in area occurred fora large portion of the total coupon immersion time. In contrast, thetime averaged area for tubercles labeled 5 and 11 appear very active.The light areas for these tubercles show where the initiation pointstarted with the actively changing area appearing dark.

In certain embodiments, the analyzing of the series of digital imagescomprises analyzing (e.g., synthesizing) dynamic activity of a tuberclein the region of interest. Using the same set of tubercles identified inFIG. 13 the growth profile for each tubercle is plotted in FIG. 14. Thedata shows rapid area growth for all tubercles except 5 and 11 over arelatively short period before reaching a plateau. If the plateau regionis considered inactive, a plot of the active time from FIG. 14 exhibitsa good correlation with the offline pit depth measurement from asubstrate (e.g., coupon). In this case, the digital imaging analysiswould track the area change for isolated individual tubercles toidentify the active period and extrapolate a pit depth based on thecalibration curve shown in FIG. 15. This analysis provides the abilityto project pit depth or corrosion rate three months later based oncorrosion data collected over a much shorter period.

In certain embodiments, the methods disclosed herein provide the abilityto identify corrosion sites, including active corrosion sites, based oncolor analysis and classification. For example, mild steel corrosion isknown to form tubercles comprising mounds of corrosion products. Thecolor of these products generally provides some insight on the moundstructure. Hematite is generally reddish brown to orange in appearancewhile magnetite generally appears blackish. The color can provideinformation related to whether a corrosion feature may be aggressive.Generally, for mild steel, a highly aggressive corrosion site colortends to be more orange-red in appearance. In some cases, a color changecan be detectable with the addition of an inhibitor causing the color toappear darker. Using a color digital imaging device, the image collectedcan be associated with the red-green-blue (“RGB”) color model. Theseindividual color planes can be extracted to view and process as well asconvert to other color models such as hue, saturation, intensity(“HSI”), which corresponds closely to how the human eye interpretscolor. An example illustrating the change in color with the addition ofan inhibitor is shown in FIG. 16 for a mild steel coupon exposed toWater A for 24 hours then treated with an inhibitor (in this instance,3DT189 as described herein).

The image shown in FIG. 16 represent the extracted red plane. Theoverall intensity of the corrosion features is higher for thenon-inhibited case compared to same coupon after addition of inhibitor.The difference is subtle but becomes clearer by binning the line profileintensity for the selected region of interest for each color plane. Theaveraged bin values are the sum of the line profiles divided by thenumber of profiles. The results for red, green, and blue are shown inFIG. 17. The dashed profiles are the cases with inhibitor added. Inaddition to the overall size not changing after addition of theinhibitor, a significant decrease in the red and green intensity occursindicating a decrease in corrosion activity. This change in color is adiscriminating factor to identify local active versus inactive corrosionsites.

In certain embodiments, the methods disclosed herein can be utilized toevaluate corrosion properties via accelerated corrosion. As discussed,pit initiation and pit growth in the presence of a corrosion inhibitoris generally a slow process, routinely taking 3 days or more to generatepits, and additional two weeks or longer to differentiate pit growthchanges with a corrosion inhibitor program. An example of a mild steelsubstrate showing pit initiation and growth is shown in FIG. 18 for aseries of digital images collected. In the absence of corrosioninhibitor, pit initiation occurred within 30 minutes. By controlling thetime duration of the substrate contacting industrial water in theindustrial water system, pit size of the corrosion features is alsocontrolled. Once the desired pit size is achieved, a corrosion inhibitorcan be added to reduce or quench the corrosion (area and/or pit) rate.The approach of initiating a desired pit size followed by addinginhibitor can accelerate the evaluation process for the overalleffectiveness of a corrosion inhibitor program.

In certain embodiments, the methods further comprise enhancing corrosionfeatures in the region of interest via adding a fluorescing moiety tothe industrial water in the industrial water system. By adding afluorescing moiety to the industrial water, the fluorescing moietyattaches or reacts with the corrosion features. Detection can be made byusing an excitation illumination source at the appropriate wavelength.Light emission can be captured by the imaging device to provide a 2D mapof the fluorescence originating from the corrosion features of thesubstrate surface.

All references, including publications, patent applications, andpatents, cited herein are hereby incorporated by reference to the sameextent as if each reference were individually and specifically indicatedto be incorporated by reference and were set forth in its entiretyherein.

The use of the terms “a” and “an” and “the” and “at least one” andsimilar referents in the context of describing the invention (especiallyin the context of the following claims) are to be construed to coverboth the singular and the plural, unless otherwise indicated herein orclearly contradicted by context. In particular, the word “series”appears in this application and should be construed to cover both thesingular and the plural, unless otherwise indicated herein or clearlycontradicted by context. The use of the term “at least one” followed bya list of one or more items (for example, “at least one of A and B”) isto be construed to mean one item selected from the listed items (A or B)or any combination of two or more of the listed items (A and B), unlessotherwise indicated herein or clearly contradicted by context. The terms“comprising,” “having,” “including,” and “containing” are to beconstrued as open-ended terms (i.e., meaning “including, but not limitedto,”) unless otherwise noted. Recitation of ranges of values herein aremerely intended to serve as a shorthand method of referring individuallyto each separate value falling within the range, unless otherwiseindicated herein, and each separate value is incorporated into thespecification as if it were individually recited herein. All methodsdescribed herein can be performed in any suitable order unless otherwiseindicated herein or otherwise clearly contradicted by context. The useof any and all examples, or exemplary language (e.g., “such as”)provided herein, is intended merely to better illuminate the inventionand does not pose a limitation on the scope of the invention unlessotherwise claimed. No language in the specification should be construedas indicating any non-claimed element as essential to the practice ofthe invention.

Preferred embodiments of this invention are described herein, includingthe best mode known to the inventors for carrying out the invention.Variations of those preferred embodiments may become apparent to thoseof ordinary skill in the art upon reading the foregoing description. Theinventors expect skilled artisans to employ such variations asappropriate, and the inventors intend for the invention to be practicedotherwise than as specifically described herein. Accordingly, thisinvention includes all modifications and equivalents of the subjectmatter recited in the claims appended hereto as permitted by applicablelaw. Moreover, any combination of the above-described elements in allpossible variations thereof is encompassed by the invention unlessotherwise indicated herein or otherwise clearly contradicted by context.

What is claimed is:
 1. A method of analyzing a substrate contactingfluid present in an industrial system, the method comprising: creating adigital image of the substrate while the substrate contacts the fluidpresent in the industrial system; defining a region of interest in thedigital image of the substrate; identifying a corrosion feature in theregion of interest in the digital image of the substrate; and analyzingthe corrosion feature in the region of interest in the digital image ofthe substrate.
 2. The method of claim 1, further comprising moving thesubstrate in the industrial system to expose a second region of interestto digital imaging; and repeating the steps of the method.
 3. The methodof claim 1, wherein the fluid is industrial water and the industrialsystem is an industrial water system.
 4. A method of analyzing asubstrate contacting fluid present in an industrial system, the methodcomprising: creating a series of digital images of the substrate whilethe substrate contacts the fluid present in the industrial system;defining a region of interest in the series of digital images of thesubstrate; identifying a corrosion feature in the region of interest inthe series of digital images of the substrate; and analyzing thecorrosion feature in the region of interest in the series of digitalimages of the substrate to determine a corrosion trend of the industrialsystem.
 5. A method of analyzing a substrate contacting industrial waterpresent in an industrial water system, the method comprising: treatingthe industrial water of the industrial water system with a corrosioninhibitor; creating a series of digital images of the substrate whilethe substrate contacts the industrial water present in the industrialwater system; defining a region of interest in the series of digitalimages of the substrate; identifying a corrosion feature in the regionof interest in the series of digital images of the substrate; analyzingthe corrosion feature in the region of interest in the series of digitalimages of the substrate to determine a corrosion trend of the industrialwater system; and acting based on the analysis of the corrosion featurein the region of interest in the series of digital images of thesubstrate.
 6. The method of claim 5, further comprising measuring aparameter of the industrial water present in the industrial water systemselected from pH, conductivity, oxidation-reduction potential, linearpolarization resistance, derivatives thereof, and combinations thereof.7. The method of claim 5, further comprising estimating the surface areaof the corrosion feature.
 8. The method of claim 7, further comprisingestimating pit depth of the corrosion feature based on the estimatedsurface area of the corrosion feature.
 9. The method of claim 7, furthercomprising estimating pit depth of the corrosion feature based on aone-dimensional measurement of the corrosion feature.
 10. The method ofclaim 5, further comprising identifying a plurality of corrosionfeatures in the region of interest.
 11. The method of claim 10, furthercomprising counting the plurality of corrosion features.
 12. The methodof claim 10, further comprising counting and tracking the plurality ofcorrosion features.
 13. The method of claim 5, wherein the substrate isa corrosion coupon.
 14. The method of claim 13, wherein the corrosioncoupon is capable of undergoing an ASTM corrosion test.
 15. The methodof claim 13, wherein the corrosion coupon is constructed of a metalselected from steel, iron, aluminum, copper, brass, nickel, and relatedalloys.
 16. The method of claim 15, wherein the metal is steel selectedfrom mild steel, stainless steel, carbon steel, and related alloys. 17.The method of claim 16, wherein the steel is mild steel.
 18. The methodof claim 5, wherein the acting comprises at least one of increasingdosage of corrosion inhibitor, selecting a different corrosioninhibitor, modifying the corrosion inhibitor, altering a physicalproperty of the industrial water system, and shutting down theindustrial water system.
 19. The method of claim 5, wherein theanalyzing of the corrosion feature of the region of interest of theseries of digital images comprises classifying corrosion on thesubstrate according to color profile of the region of interest orsubregion thereof of at least one of the series of digital images. 20.The method of claim 5, further comprising moving the substrate in theindustrial system to expose a second region of interest to digitalimaging; and repeating the steps of the method.